{"id":"W4321371507","doi":"10.3390/make5010016","title":"A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Corrosion; Pipeline transport; Artificial neural network; Downtime; Reliability (semiconductor); Cathodic protection; Submarine pipeline; Environmental science; Fossil fuel; Pipeline (software); Petroleum engineering; Engineering; Reliability engineering; Computer science; Materials science; Metallurgy; Geotechnical engineering; Environmental engineering; Waste management; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006720236,0.001094225,0.0007373435,0.002282093,0.0002847302,0.0009885131,0.0007098077,0.001135573,0.001108611],"category_scores_gemma":[0.001892141,0.00024521,0.0005024945,0.001188586,0.0003188824,0.002017425,0.0004955617,0.000452241,0.0003338859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006424182,"about_ca_system_score_gemma":0.0004229631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006107957,"about_ca_topic_score_gemma":0.004431274,"domain_scores_codex":[0.9994187,0.00006946326,0.00005557532,0.0001996658,0.0001942537,0.00006247954],"domain_scores_gemma":[0.9993631,0.0001371792,0.0001434089,0.00003708757,0.0002774566,0.0000417233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004957899,0.0003579192,0.062438,0.000283748,0.0002125507,0.0003917531,0.0001062273,0.5734239,0.02237937,0.001989427,0.001799462,0.3361218],"study_design_scores_gemma":[0.000003216011,0.00008391771,0.007111453,0.000009265358,0.00002658444,0.00005364319,0.00001531219,0.9898208,0.002124678,0.0004190513,0.0003181746,0.00001386961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2336535,0.001166393,0.7582881,0.0002209626,0.0001522718,0.0001648327,0.0007323136,0.001094888,0.004526787],"genre_scores_gemma":[0.9567989,0.0003894622,0.03985358,0.00003196349,0.0000919361,0.0000998194,0.0004832218,0.00002925338,0.002221862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006107957,"threshold_uncertainty_score":0.0121448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05312106828459529,"score_gpt":0.3216352058030846,"score_spread":0.2685141375184893,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}